Instructions to use a7mid/ArbicSummarization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use a7mid/ArbicSummarization with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("a7mid/ArbicSummarization") model = AutoModelForSeq2SeqLM.from_pretrained("a7mid/ArbicSummarization", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f0919cff09df7af7e0d6567efeae241e12b4b235fed131c0fcb38a44261b739a
- Size of remote file:
- 1.32 MB
- SHA256:
- cbb59d772bc9bb2da5dc4a73a00c61c0912c6d2596aad970fa2cd3d69898b245
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.